使用深度学习集成图像细胞术对结外NK/T细胞淋巴瘤患者的血细胞进行生物物理分析的简单方法

BMEMat Pub Date : 2024-11-22 DOI:10.1002/bmm2.12128
Seongcheol Park, Sang Eun Yoon, Youngho Song, Changyu Tian, Changi Baek, Hyunji Cho, Won Seog Kim, Seok Jin Kim, Soo-Yeon Cho
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摘要

结外NK/T细胞淋巴瘤(ENKTL)由于其侵袭性和高复发率,在有效的治疗过程中提出了重大挑战。目前迫切需要建立一个强大的统计模型,通过动态量化针对不同阶段淋巴瘤的生物标志物来预测治疗效果。最近的分析,如测序和微生物组测试,仅用于了解淋巴瘤进展和临床治疗反应。然而,这些方法受到定量分析能力、周转时间长和缺乏单细胞分辨率的限制,而单细胞分辨率对于理解淋巴瘤的异质性至关重要。在这项研究中,我们开发了一种深度学习增强图像细胞术(DLIC)来研究新诊断(ND) ENKTL患者外周血单个核细胞(PBMCs)的生物物理异质性。我们建立了一个由23名ND ENKTL患者组成的庞大队列,根据他们的系列治疗时间表将他们分为治疗中期(n = 21)和治疗结束(n = 19)阶段。利用基本光学显微镜和商用微芯片,我们以高通量分析了超过270,000个单个pbmc,通过基于人工智能的纳米光子计算,以完全无标签和量化的方式分析了它们的尺寸,偏心率和折射率。我们观察到这三种生物物理指标在不同治疗阶段和复发状态下的明显异质性,揭示了表型之间坚实的机制相关性。我们建立了ENKTL患者的三维单细胞分布图,并创建了量化职业容积变化的标准。利用这个广泛的数据库,DLIC在临床环境中提供现场分析,通过对患者pbmc进行无标签生物物理分析,促进治疗评估和预后预测,直接提取,无需额外的样品制备。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A simple approach to biophysical profiling of blood cells in extranodal NK/T cell lymphoma patients using deep learning-integrated image cytometry

A simple approach to biophysical profiling of blood cells in extranodal NK/T cell lymphoma patients using deep learning-integrated image cytometry

Extranodal NK/T cell lymphoma (ENKTL) poses significant challenges in efficient treatment processes due to its aggressive nature and high recurrence rates. There is a critical need to develop a robust statistical model to predict treatment efficacy by dynamically quantifying biomarkers tailored to various stages of lymphoma. Recent analytics such as sequencing and microbiome tests have only been utilized to understand lymphoma progression and treatment response in clinical settings. However, these methods are limited by their quantitative analysis capabilities, long turnaround times, and lack of single-cell resolution, which are essential for understanding the heterogeneous nature of lymphoma. In this study, we developed a deep learning-enhanced image cytometry (DLIC) to investigate biophysical heterogeneities in peripheral blood mononuclear cells (PBMCs) from newly diagnosed (ND) ENKTL patients. We established a substantial cohort of 23 ND ENKTL patients, categorizing them into interim of treatment (n = 21) and end of treatment (n = 19) stages along their serial treatment timelines. Using a basic optical microscope and a commercial microchip, we analyzed over 270,000 single PBMCs in high-throughput, profiling their size, eccentricity, and refractive index in a completely label-free and quantified manner through AI-based nanophotonic computation. We observed distinct heterogeneity variations in these three biophysical indicators across treatment stages and relapse statuses, revealing solid mechanistic correlations among the phenotypes. We established a three-dimensional single-cell distribution map for ENKTL patients and created a standard for quantifying the change in occupational volume. Leveraging this extensive database, DLIC offers on-site analytics in clinical settings, facilitating treatment assessment and prognosis prediction through label-free biophysical analysis of patient PBMCs, extracted directly without additional sample preparation.

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